Specialized curricula for training vision language models in retinal image analysis.

ABSTRACT: Clinicians spend significant time reviewing medical images and transcribing findings. By integrating visual and textual data, foundation models have the potential to reduce workloads and boost efficiency, yet their practical clinical value remains uncertain. In this study, we find that OpenAI's ChatGPT-4o and two medical vision-language models (VLMs) significantly underperform ophthalmologists in key tasks for age-related macular degeneration (AMD). To address this, we developed a dedicated training curriculum, designed by domain specialists, to optimize VLMs for tasks related to clinical decision making. The resulting model, RetinaVLM-Specialist, significantly outperforms foundation medical VLMs and ChatGPT-4o in AMD disease staging (F1: 0.63 vs. 0.33) and referral (0.67 vs. 0.50), achiev

SUBMITTER: Holland R 

PROVIDER: S-EPMC12365215 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature